Taxonomic variation
genome_counts_filt %>%
mutate_at(vars(-genome),~./sum(.)) %>% #apply TSS nornalisation
pivot_longer(-genome, names_to = "sample", values_to = "count") %>% #reduce to minimum number of columns
left_join(., genome_metadata, by = join_by(genome == genome)) %>% #append genome metadata
left_join(., sample_metadata, by = join_by(sample == sample)) %>% #append sample metadata
filter(count > 0) %>% #filter 0 counts
left_join(core_microbiota,by="genome") %>%
group_by(type,sample) %>%
summarise(fraction=sum(count)) %>%
group_by(type) %>%
summarise(mean(fraction))
`summarise()` has grouped output by 'type'. You can override using the `.groups` argument.
# A tibble: 4 × 2
type `mean(fraction)`
<chr> <dbl>
1 core 0.870
2 endemic 0.0882
3 marginal 0.0407
4 <NA> 0.0746
Functional differences between fractions
genome_gifts %>%
to.elements(., GIFT_db) %>%
to.functions(., GIFT_db) %>%
to.domains(., GIFT_db) %>%
as.data.frame() %>%
mutate(mci=(Biosynthesis+Degradation)/2) %>%
rownames_to_column(var="genome") %>%
select(genome,mci) %>%
left_join(core_microbiota %>% select(genome,type),by="genome") %>%
pivot_longer(!c(genome,type),names_to = "trait",values_to = "value") %>%
filter(!is.na(type)) %>%
ggplot(aes(x=type, y=value, group=type))+
geom_boxplot()+
facet_grid(~trait, scales="free")

genome_gifts %>%
to.elements(., GIFT_db) %>%
to.functions(., GIFT_db) %>%
to.domains(., GIFT_db) %>%
as.data.frame() %>%
mutate(mci=(Biosynthesis+Degradation)/2) %>%
rownames_to_column(var="genome") %>%
select(genome,mci) %>%
left_join(core_microbiota %>% select(genome,type),by="genome") %>%
pivot_longer(!c(genome,type),names_to = "trait",values_to = "value") %>%
filter(!is.na(type)) %>%
group_by(trait) %>%
pairwise_wilcox_test(value ~ type, p.adjust.method = "BH")
# A tibble: 3 × 10
trait .y. group1 group2 n1 n2 statistic p p.adj p.adj.signif
* <chr> <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
1 mci value core endemic 389 85 21482 0.0000152 0.0000228 ****
2 mci value core marginal 389 62 16678 0.00000126 0.00000378 ****
3 mci value endemic marginal 85 62 2903 0.294 0.294 ns
genome_gifts %>%
to.elements(., GIFT_db) %>%
to.functions(., GIFT_db) %>%
to.domains(., GIFT_db) %>%
as.data.frame() %>%
rownames_to_column(var="genome") %>%
left_join(core_microbiota %>% select(genome,type),by="genome") %>%
pivot_longer(!c(genome,type),names_to = "trait",values_to = "value") %>%
filter(!is.na(type)) %>%
group_by(trait) %>%
pairwise_wilcox_test(value ~ type, p.adjust.method = "BH")
# A tibble: 6 × 10
trait .y. group1 group2 n1 n2 statistic p p.adj p.adj.signif
* <chr> <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
1 Biosynthesis value core endemic 389 85 21545 0.0000118 0.0000177 ****
2 Biosynthesis value core marginal 389 62 17034. 0.000000179 0.000000537 ****
3 Biosynthesis value endemic marginal 85 62 2986 0.169 0.169 ns
4 Degradation value core endemic 389 85 20437 0.000644 0.002 **
5 Degradation value core marginal 389 62 15153 0.001 0.002 **
6 Degradation value endemic marginal 85 62 2687 0.84 0.84 ns
genome_gifts %>%
to.elements(., GIFT_db) %>%
to.functions(., GIFT_db) %>%
to.domains(., GIFT_db) %>%
as.data.frame() %>%
rownames_to_column(var="genome") %>%
left_join(core_microbiota %>% select(genome,type),by="genome") %>%
pivot_longer(!c(genome,type),names_to = "trait",values_to = "value") %>%
filter(!is.na(type)) %>%
ggplot(aes(x=type, y=value, group=type))+
geom_boxplot()+
facet_grid(~trait, scales="free")

genome_gifts %>%
to.elements(., GIFT_db) %>%
to.functions(., GIFT_db) %>%
as.data.frame() %>%
rownames_to_column(var="genome") %>%
left_join(core_microbiota %>% select(genome,type),by="genome") %>%
pivot_longer(!c(genome,type),names_to = "trait",values_to = "value") %>%
filter(!is.na(type),
trait %in% c("B01","B02","B03","B04","B06","B07","B08","D01","D02","D03","D05","D06","D07","D09")) %>%
group_by(trait) %>%
pairwise_wilcox_test(value ~ type, p.adjust.method = "BH") %>%
print(n=100)
# A tibble: 42 × 10
trait .y. group1 group2 n1 n2 statistic p p.adj p.adj.signif
* <chr> <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
1 B01 value core endemic 389 85 20668. 0.000301 0.000451 ***
2 B01 value core marginal 389 62 16226. 0.0000123 0.0000369 ****
3 B01 value endemic marginal 85 62 2914. 0.274 0.274 ns
4 B02 value core endemic 389 85 21470. 0.0000159 0.0000238 ****
5 B02 value core marginal 389 62 16529 0.00000274 0.00000822 ****
6 B02 value endemic marginal 85 62 2752. 0.649 0.649 ns
7 B03 value core endemic 389 85 18336. 0.112 0.168 ns
8 B03 value core marginal 389 62 13706. 0.081 0.168 ns
9 B03 value endemic marginal 85 62 2744. 0.666 0.666 ns
10 B04 value core endemic 389 85 16912. 0.74 0.74 ns
11 B04 value core marginal 389 62 15036 0.002 0.005 **
12 B04 value endemic marginal 85 62 3186. 0.03 0.046 *
13 B06 value core endemic 389 85 22249 0.000000582 0.000000873 ****
14 B06 value core marginal 389 62 17100 0.000000123 0.000000369 ****
15 B06 value endemic marginal 85 62 2915 0.273 0.273 ns
16 B07 value core endemic 389 85 22042 0.00000147 0.00000441 ****
17 B07 value core marginal 389 62 16465 0.0000038 0.0000057 ****
18 B07 value endemic marginal 85 62 2798. 0.525 0.525 ns
19 B08 value core endemic 389 85 20472. 0.000563 0.000844 ***
20 B08 value core marginal 389 62 16182. 0.0000148 0.0000444 ****
21 B08 value endemic marginal 85 62 3007 0.145 0.145 ns
22 D01 value core endemic 389 85 19124. 0.016 0.047 *
23 D01 value core marginal 389 62 12628. 0.527 0.527 ns
24 D01 value endemic marginal 85 62 2368 0.238 0.357 ns
25 D02 value core endemic 389 85 21914. 0.00000254 0.00000762 ****
26 D02 value core marginal 389 62 15312. 0.000644 0.000966 ***
27 D02 value endemic marginal 85 62 2430 0.422 0.422 ns
28 D03 value core endemic 389 85 19320. 0.015 0.022 *
29 D03 value core marginal 389 62 14923 0.003 0.008 **
30 D03 value endemic marginal 85 62 2743 0.673 0.673 ns
31 D05 value core endemic 389 85 20434. 0.000649 0.000974 ***
32 D05 value core marginal 389 62 15667 0.000154 0.000462 ***
33 D05 value endemic marginal 85 62 2868. 0.363 0.363 ns
34 D06 value core endemic 389 85 19654 0.006 0.018 *
35 D06 value core marginal 389 62 12642. 0.538 0.538 ns
36 D06 value endemic marginal 85 62 2322 0.219 0.328 ns
37 D07 value core endemic 389 85 16382 0.895 0.972 ns
38 D07 value core marginal 389 62 12092. 0.972 0.972 ns
39 D07 value endemic marginal 85 62 2690. 0.828 0.972 ns
40 D09 value core endemic 389 85 17899 0.225 0.225 ns
41 D09 value core marginal 389 62 15196. 0.000804 0.002 **
42 D09 value endemic marginal 85 62 3028. 0.117 0.176 ns
genome_gifts %>%
to.elements(., GIFT_db) %>%
to.functions(., GIFT_db) %>%
as.data.frame() %>%
rownames_to_column(var="genome") %>%
left_join(core_microbiota %>% select(genome,type),by="genome") %>%
pivot_longer(!c(genome,type),names_to = "trait",values_to = "value") %>%
filter(!is.na(type)) %>%
ggplot(aes(x=type, y=value, group=type))+
geom_boxplot()+
facet_grid(~trait, scales="free")

Functional differences between high and low endemisms
genome_gifts %>%
to.elements(., GIFT_db) %>%
to.functions(., GIFT_db) %>%
to.domains(., GIFT_db) %>%
as.data.frame() %>%
mutate(mci=(Biosynthesis+Degradation)/2) %>%
rownames_to_column(var="genome") %>%
select(genome,mci) %>%
left_join(core_microbiota %>% select(genome,type_prevalence_environment, type),by="genome") %>%
filter(type == "endemic") %>%
select(-type) %>%
pivot_longer(!c(genome,type_prevalence_environment),names_to = "trait",values_to = "value") %>%
filter(!is.na(type_prevalence_environment)) %>%
ggplot(aes(x=type_prevalence_environment, y=value, group=type_prevalence_environment))+
geom_boxplot()+
facet_grid(~trait, scales="free")

genome_gifts %>%
to.elements(., GIFT_db) %>%
to.functions(., GIFT_db) %>%
to.domains(., GIFT_db) %>%
as.data.frame() %>%
mutate(mci=(Biosynthesis+Degradation)/2) %>%
rownames_to_column(var="genome") %>%
select(genome,mci) %>%
left_join(core_microbiota %>% select(genome,type_prevalence_environment, type),by="genome") %>%
filter(type == "endemic") %>%
select(-type) %>%
pivot_longer(!c(genome,type_prevalence_environment),names_to = "trait",values_to = "value") %>%
filter(!is.na(type_prevalence_environment)) %>%
group_by(trait) %>%
pairwise_wilcox_test(value ~ type_prevalence_environment, p.adjust.method = "BH")
# A tibble: 1 × 10
trait .y. group1 group2 n1 n2 statistic p p.adj p.adj.signif
* <chr> <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
1 mci value high low 47 38 552 0.002 0.002 **
genome_gifts %>%
to.elements(., GIFT_db) %>%
to.functions(., GIFT_db) %>%
as.data.frame() %>%
rownames_to_column(var="genome") %>%
left_join(core_microbiota %>% select(genome,type_prevalence_environment, type),by="genome") %>%
filter(type == "endemic") %>%
select(-type) %>%
pivot_longer(!c(genome,type_prevalence_environment),names_to = "trait",values_to = "value") %>%
filter(!is.na(type_prevalence_environment),
trait %in% c("B01","B02","B03","B04","B06","B07","B08","D01","D02","D03","D05","D06","D07","D09")) %>%
ggplot(aes(x=type_prevalence_environment, y=value, group=type_prevalence_environment))+
geom_boxplot()+
facet_grid(~trait, scales="free")

genome_gifts %>%
to.elements(., GIFT_db) %>%
to.functions(., GIFT_db) %>%
as.data.frame() %>%
rownames_to_column(var="genome") %>%
left_join(core_microbiota %>% select(genome,type_prevalence_environment, type),by="genome") %>%
filter(type == "endemic") %>%
select(-type) %>%
pivot_longer(!c(genome,type_prevalence_environment),names_to = "trait",values_to = "value") %>%
filter(!is.na(type_prevalence_environment),
trait %in% c("B01","B02","B03","B04","B06","B07","B08","D01","D02","D03","D05","D06","D07","D09")) %>%
group_by(trait) %>%
pairwise_wilcox_test(value ~ type_prevalence_environment, p.adjust.method = "BH") %>%
print(n=100)
# A tibble: 14 × 10
trait .y. group1 group2 n1 n2 statistic p p.adj p.adj.signif
* <chr> <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
1 B01 value high low 47 38 814. 0.485 0.485 ns
2 B02 value high low 47 38 588. 0.007 0.007 **
3 B03 value high low 47 38 668 0.045 0.045 *
4 B04 value high low 47 38 684 0.065 0.065 ns
5 B06 value high low 47 38 635 0.023 0.023 *
6 B07 value high low 47 38 639 0.025 0.025 *
7 B08 value high low 47 38 740. 0.179 0.179 ns
8 D01 value high low 47 38 750. 0.143 0.143 ns
9 D02 value high low 47 38 614 0.014 0.014 *
10 D03 value high low 47 38 592 0.008 0.008 **
11 D05 value high low 47 38 637 0.024 0.024 *
12 D06 value high low 47 38 682. 0.061 0.061 ns
13 D07 value high low 47 38 660. 0.04 0.04 *
14 D09 value high low 47 38 523 0.000929 0.000929 ***
Functional variation
Core
# A tibble: 0 × 5
# ℹ 5 variables: gift <chr>, high <dbl>, low <dbl>, p_value <dbl>, p_adjust <dbl>


Wilcoxon rank sum exact test
data: mci by environment
W = 55, p-value = 0.01643
alternative hypothesis: true location shift is not equal to 0
# A tibble: 0 × 7
# ℹ 7 variables: gift <chr>, high <dbl>, low <dbl>, p_value <dbl>, p_adjust <dbl>, difference <dbl>, significance <chr>

| term |
df |
SumOfSqs |
R2 |
statistic |
p.value |
| environment |
1 |
0.7158447 |
0.06965384 |
2.136654 |
0.051 |
| river |
2 |
0.8505330 |
0.08275942 |
1.269336 |
0.223 |
| Residual |
26 |
8.7107971 |
0.84758674 |
NA |
NA |
| Total |
29 |
10.2771749 |
1.00000000 |
NA |
NA |


Endemic


Wilcoxon rank sum exact test
data: mci by environment
W = 24, p-value = 8.979e-05
alternative hypothesis: true location shift is not equal to 0
| gift |
high |
low |
p_value |
p_adjust |
| B0104 |
0.318371850 |
0.477248051 |
4.266185e-03 |
8.738798e-03 |
| B0106 |
0.652417204 |
0.750373702 |
2.349615e-02 |
3.825655e-02 |
| B0204 |
0.272751511 |
0.411834564 |
9.967024e-04 |
2.751765e-03 |
| B0205 |
0.349827863 |
0.607824698 |
2.150627e-04 |
7.803705e-04 |
| B0207 |
0.349413509 |
0.607091324 |
8.978999e-05 |
4.072617e-04 |
| B0208 |
0.277418167 |
0.571441654 |
1.125340e-04 |
4.610265e-04 |
| B0209 |
0.418950756 |
0.654470027 |
1.665215e-03 |
3.916339e-03 |
| B0210 |
0.314626667 |
0.599130873 |
2.701874e-03 |
5.815898e-03 |
| B0211 |
0.505796266 |
0.763739750 |
2.514223e-06 |
6.450676e-05 |
| B0212 |
0.341025004 |
0.546199073 |
4.939623e-03 |
9.802064e-03 |
| B0213 |
0.415890582 |
0.550634314 |
1.643172e-02 |
2.782438e-02 |
| B0215 |
0.255319614 |
0.554385824 |
5.777813e-04 |
1.834456e-03 |
| B0216 |
0.105448827 |
0.426833768 |
2.041033e-05 |
1.993932e-04 |
| B0217 |
0.281630700 |
0.400857555 |
6.955501e-04 |
2.154509e-03 |
| B0220 |
0.108696049 |
0.075493630 |
9.874823e-03 |
1.717949e-02 |
| B0221 |
0.255637379 |
0.383346961 |
9.874823e-03 |
1.717949e-02 |
| B0303 |
0.168217937 |
0.258794974 |
2.643028e-04 |
8.833279e-04 |
| B0309 |
0.028042752 |
0.096603777 |
7.399332e-03 |
1.408868e-02 |
| B0310 |
0.000000000 |
0.039314500 |
8.257612e-03 |
1.519879e-02 |
| B0402 |
0.556281087 |
0.493520756 |
1.408119e-03 |
3.804919e-03 |
| B0601 |
0.382208213 |
0.577022449 |
2.654761e-05 |
2.408248e-04 |
| B0602 |
0.506114317 |
0.745114749 |
1.403581e-04 |
5.401660e-04 |
| B0603 |
0.260255972 |
0.467609340 |
5.618966e-05 |
3.161839e-04 |
| B0701 |
0.432395583 |
0.679531792 |
8.978999e-05 |
4.072617e-04 |
| B0703 |
0.022340365 |
0.156111414 |
5.726164e-05 |
3.161839e-04 |
| B0704 |
0.312937738 |
0.598325101 |
3.432236e-05 |
2.564082e-04 |
| B0705 |
0.184992803 |
0.398754859 |
1.554950e-05 |
1.645656e-04 |
| B0706 |
0.336719078 |
0.509754909 |
7.543545e-03 |
1.408868e-02 |
| B0707 |
0.584533168 |
0.687871927 |
7.543545e-03 |
1.408868e-02 |
| B0710 |
0.016909779 |
0.108809582 |
1.581108e-03 |
3.916339e-03 |
| B0711 |
0.208154526 |
0.416638817 |
7.121052e-05 |
3.617495e-04 |
| B0712 |
0.099046619 |
0.204586247 |
5.703508e-03 |
1.114378e-02 |
| B0804 |
0.456763954 |
0.763616891 |
1.177172e-05 |
1.359099e-04 |
| B0805 |
0.042739966 |
0.173618219 |
5.802053e-07 |
4.360009e-05 |
| B0901 |
0.100157586 |
0.041913672 |
1.665215e-03 |
3.916339e-03 |
| B0903 |
0.000000000 |
0.019080084 |
2.539636e-06 |
6.450676e-05 |
| B1004 |
0.125149306 |
0.140982835 |
8.964396e-03 |
1.603490e-02 |
| B1012 |
0.001356128 |
0.019240248 |
1.619167e-04 |
6.048064e-04 |
| B1014 |
0.022601362 |
0.000000000 |
2.539636e-06 |
6.450676e-05 |
| D0104 |
0.058683884 |
0.159932473 |
2.306458e-03 |
5.050347e-03 |
| D0201 |
0.092234219 |
0.260028653 |
8.340902e-04 |
2.407488e-03 |
| D0202 |
0.160795013 |
0.371123533 |
5.618966e-05 |
3.161839e-04 |
| D0203 |
0.342605111 |
0.471551663 |
3.432236e-05 |
2.564082e-04 |
| D0204 |
0.250243216 |
0.395633777 |
2.306458e-03 |
5.050347e-03 |
| D0205 |
0.063752861 |
0.169920437 |
7.121052e-05 |
3.617495e-04 |
| D0206 |
0.147551315 |
0.443090128 |
5.618966e-05 |
3.161839e-04 |
| D0207 |
0.412156668 |
0.558207530 |
2.090231e-02 |
3.447523e-02 |
| D0208 |
0.155823369 |
0.301738922 |
5.618966e-05 |
3.161839e-04 |
| D0209 |
0.131997770 |
0.352678185 |
6.549873e-06 |
9.242598e-05 |
| D0210 |
0.133702102 |
0.284613983 |
1.177172e-05 |
1.359099e-04 |
| D0212 |
0.133552981 |
0.422562064 |
6.549873e-06 |
9.242598e-05 |
| D0213 |
0.126218478 |
0.306630447 |
8.340902e-04 |
2.407488e-03 |
| D0305 |
0.366492686 |
0.423996527 |
4.939623e-03 |
9.802064e-03 |
| D0306 |
0.146105878 |
0.434641186 |
6.549873e-06 |
9.242598e-05 |
| D0501 |
0.790847790 |
0.872992541 |
1.805380e-02 |
3.016885e-02 |
| D0502 |
0.480358478 |
0.285918054 |
4.781407e-04 |
1.557022e-03 |
| D0503 |
0.125879327 |
0.000000000 |
6.866156e-07 |
4.360009e-05 |
| D0505 |
0.286597240 |
0.350858031 |
8.641809e-03 |
1.567871e-02 |
| D0506 |
0.211360464 |
0.391670686 |
6.549873e-06 |
9.242598e-05 |
| D0507 |
0.110052668 |
0.210004157 |
1.125340e-04 |
4.610265e-04 |
| D0509 |
0.440041988 |
0.526487614 |
1.125601e-02 |
1.931774e-02 |
| D0510 |
0.000000000 |
0.013573683 |
2.308808e-04 |
7.924829e-04 |
| D0511 |
0.611751724 |
0.767405683 |
1.665215e-03 |
3.916339e-03 |
| D0513 |
0.224858211 |
0.438902945 |
3.155375e-03 |
6.678878e-03 |
| D0601 |
0.034174614 |
0.141369358 |
2.997491e-02 |
4.758517e-02 |
| D0602 |
0.000000000 |
0.030499587 |
2.308808e-04 |
7.924829e-04 |
| D0606 |
0.003034342 |
0.043637689 |
5.152105e-05 |
3.161839e-04 |
| D0607 |
0.008552574 |
0.041697052 |
2.096373e-03 |
4.840715e-03 |
| D0608 |
0.000000000 |
0.002444638 |
3.625355e-03 |
7.547870e-03 |
| D0609 |
0.158687978 |
0.084565489 |
1.403581e-04 |
5.401660e-04 |
| D0610 |
0.000000000 |
0.007160308 |
1.522953e-03 |
3.916339e-03 |
| D0702 |
0.455688142 |
0.326464654 |
2.635404e-02 |
4.236663e-02 |
| D0705 |
0.323241782 |
0.258159545 |
2.306458e-03 |
5.050347e-03 |
| D0706 |
0.059976159 |
0.148351231 |
1.665215e-03 |
3.916339e-03 |
| D0807 |
0.015074471 |
0.041747020 |
8.978999e-05 |
4.072617e-04 |
| D0816 |
0.043641253 |
0.091390659 |
8.340902e-04 |
2.407488e-03 |
| D0817 |
0.005827776 |
0.019925548 |
1.445050e-03 |
3.823361e-03 |
| D0907 |
0.162914556 |
0.328681837 |
9.967024e-04 |
2.751765e-03 |
| D0908 |
0.305403136 |
0.623522292 |
3.432236e-05 |
2.564082e-04 |
| D0910 |
0.124407773 |
0.396891803 |
1.125340e-04 |
4.610265e-04 |
| gift |
high |
low |
p_value |
p_adjust |
difference |
significance |
| B0104 |
0.318371850 |
0.477248051 |
4.266185e-03 |
8.738798e-03 |
-0.158876201 |
significant |
| B0106 |
0.652417204 |
0.750373702 |
2.349615e-02 |
3.825655e-02 |
-0.097956497 |
significant |
| B0204 |
0.272751511 |
0.411834564 |
9.967024e-04 |
2.751765e-03 |
-0.139083053 |
significant |
| B0205 |
0.349827863 |
0.607824698 |
2.150627e-04 |
7.803705e-04 |
-0.257996835 |
significant |
| B0207 |
0.349413509 |
0.607091324 |
8.978999e-05 |
4.072617e-04 |
-0.257677815 |
significant |
| B0208 |
0.277418167 |
0.571441654 |
1.125340e-04 |
4.610265e-04 |
-0.294023487 |
significant |
| B0209 |
0.418950756 |
0.654470027 |
1.665215e-03 |
3.916339e-03 |
-0.235519272 |
significant |
| B0210 |
0.314626667 |
0.599130873 |
2.701874e-03 |
5.815898e-03 |
-0.284504206 |
significant |
| B0211 |
0.505796266 |
0.763739750 |
2.514223e-06 |
6.450676e-05 |
-0.257943484 |
significant |
| B0212 |
0.341025004 |
0.546199073 |
4.939623e-03 |
9.802064e-03 |
-0.205174069 |
significant |
| B0213 |
0.415890582 |
0.550634314 |
1.643172e-02 |
2.782438e-02 |
-0.134743732 |
significant |
| B0215 |
0.255319614 |
0.554385824 |
5.777813e-04 |
1.834456e-03 |
-0.299066210 |
significant |
| B0216 |
0.105448827 |
0.426833768 |
2.041033e-05 |
1.993932e-04 |
-0.321384940 |
significant |
| B0217 |
0.281630700 |
0.400857555 |
6.955501e-04 |
2.154509e-03 |
-0.119226854 |
significant |
| B0220 |
0.108696049 |
0.075493630 |
9.874823e-03 |
1.717949e-02 |
0.033202420 |
significant |
| B0221 |
0.255637379 |
0.383346961 |
9.874823e-03 |
1.717949e-02 |
-0.127709582 |
significant |
| B0303 |
0.168217937 |
0.258794974 |
2.643028e-04 |
8.833279e-04 |
-0.090577037 |
significant |
| B0309 |
0.028042752 |
0.096603777 |
7.399332e-03 |
1.408868e-02 |
-0.068561026 |
significant |
| B0310 |
0.000000000 |
0.039314500 |
8.257612e-03 |
1.519879e-02 |
-0.039314500 |
significant |
| B0402 |
0.556281087 |
0.493520756 |
1.408119e-03 |
3.804919e-03 |
0.062760331 |
significant |
| B0601 |
0.382208213 |
0.577022449 |
2.654761e-05 |
2.408248e-04 |
-0.194814235 |
significant |
| B0602 |
0.506114317 |
0.745114749 |
1.403581e-04 |
5.401660e-04 |
-0.239000432 |
significant |
| B0603 |
0.260255972 |
0.467609340 |
5.618966e-05 |
3.161839e-04 |
-0.207353368 |
significant |
| B0701 |
0.432395583 |
0.679531792 |
8.978999e-05 |
4.072617e-04 |
-0.247136209 |
significant |
| B0703 |
0.022340365 |
0.156111414 |
5.726164e-05 |
3.161839e-04 |
-0.133771050 |
significant |
| B0704 |
0.312937738 |
0.598325101 |
3.432236e-05 |
2.564082e-04 |
-0.285387364 |
significant |
| B0705 |
0.184992803 |
0.398754859 |
1.554950e-05 |
1.645656e-04 |
-0.213762056 |
significant |
| B0706 |
0.336719078 |
0.509754909 |
7.543545e-03 |
1.408868e-02 |
-0.173035831 |
significant |
| B0707 |
0.584533168 |
0.687871927 |
7.543545e-03 |
1.408868e-02 |
-0.103338760 |
significant |
| B0710 |
0.016909779 |
0.108809582 |
1.581108e-03 |
3.916339e-03 |
-0.091899802 |
significant |
| B0711 |
0.208154526 |
0.416638817 |
7.121052e-05 |
3.617495e-04 |
-0.208484291 |
significant |
| B0712 |
0.099046619 |
0.204586247 |
5.703508e-03 |
1.114378e-02 |
-0.105539628 |
significant |
| B0804 |
0.456763954 |
0.763616891 |
1.177172e-05 |
1.359099e-04 |
-0.306852936 |
significant |
| B0805 |
0.042739966 |
0.173618219 |
5.802053e-07 |
4.360009e-05 |
-0.130878253 |
significant |
| B0901 |
0.100157586 |
0.041913672 |
1.665215e-03 |
3.916339e-03 |
0.058243913 |
significant |
| B0903 |
0.000000000 |
0.019080084 |
2.539636e-06 |
6.450676e-05 |
-0.019080084 |
significant |
| B1004 |
0.125149306 |
0.140982835 |
8.964396e-03 |
1.603490e-02 |
-0.015833529 |
significant |
| B1012 |
0.001356128 |
0.019240248 |
1.619167e-04 |
6.048064e-04 |
-0.017884120 |
significant |
| B1014 |
0.022601362 |
0.000000000 |
2.539636e-06 |
6.450676e-05 |
0.022601362 |
significant |
| D0104 |
0.058683884 |
0.159932473 |
2.306458e-03 |
5.050347e-03 |
-0.101248589 |
significant |
| D0201 |
0.092234219 |
0.260028653 |
8.340902e-04 |
2.407488e-03 |
-0.167794434 |
significant |
| D0202 |
0.160795013 |
0.371123533 |
5.618966e-05 |
3.161839e-04 |
-0.210328521 |
significant |
| D0203 |
0.342605111 |
0.471551663 |
3.432236e-05 |
2.564082e-04 |
-0.128946551 |
significant |
| D0204 |
0.250243216 |
0.395633777 |
2.306458e-03 |
5.050347e-03 |
-0.145390561 |
significant |
| D0205 |
0.063752861 |
0.169920437 |
7.121052e-05 |
3.617495e-04 |
-0.106167576 |
significant |
| D0206 |
0.147551315 |
0.443090128 |
5.618966e-05 |
3.161839e-04 |
-0.295538813 |
significant |
| D0207 |
0.412156668 |
0.558207530 |
2.090231e-02 |
3.447523e-02 |
-0.146050862 |
significant |
| D0208 |
0.155823369 |
0.301738922 |
5.618966e-05 |
3.161839e-04 |
-0.145915553 |
significant |
| D0209 |
0.131997770 |
0.352678185 |
6.549873e-06 |
9.242598e-05 |
-0.220680415 |
significant |
| D0210 |
0.133702102 |
0.284613983 |
1.177172e-05 |
1.359099e-04 |
-0.150911881 |
significant |
| D0212 |
0.133552981 |
0.422562064 |
6.549873e-06 |
9.242598e-05 |
-0.289009083 |
significant |
| D0213 |
0.126218478 |
0.306630447 |
8.340902e-04 |
2.407488e-03 |
-0.180411970 |
significant |
| D0305 |
0.366492686 |
0.423996527 |
4.939623e-03 |
9.802064e-03 |
-0.057503841 |
significant |
| D0306 |
0.146105878 |
0.434641186 |
6.549873e-06 |
9.242598e-05 |
-0.288535308 |
significant |
| D0501 |
0.790847790 |
0.872992541 |
1.805380e-02 |
3.016885e-02 |
-0.082144750 |
significant |
| D0502 |
0.480358478 |
0.285918054 |
4.781407e-04 |
1.557022e-03 |
0.194440424 |
significant |
| D0503 |
0.125879327 |
0.000000000 |
6.866156e-07 |
4.360009e-05 |
0.125879327 |
significant |
| D0505 |
0.286597240 |
0.350858031 |
8.641809e-03 |
1.567871e-02 |
-0.064260791 |
significant |
| D0506 |
0.211360464 |
0.391670686 |
6.549873e-06 |
9.242598e-05 |
-0.180310222 |
significant |
| D0507 |
0.110052668 |
0.210004157 |
1.125340e-04 |
4.610265e-04 |
-0.099951489 |
significant |
| D0509 |
0.440041988 |
0.526487614 |
1.125601e-02 |
1.931774e-02 |
-0.086445625 |
significant |
| D0510 |
0.000000000 |
0.013573683 |
2.308808e-04 |
7.924829e-04 |
-0.013573683 |
significant |
| D0511 |
0.611751724 |
0.767405683 |
1.665215e-03 |
3.916339e-03 |
-0.155653959 |
significant |
| D0513 |
0.224858211 |
0.438902945 |
3.155375e-03 |
6.678878e-03 |
-0.214044734 |
significant |
| D0601 |
0.034174614 |
0.141369358 |
2.997491e-02 |
4.758517e-02 |
-0.107194744 |
significant |
| D0602 |
0.000000000 |
0.030499587 |
2.308808e-04 |
7.924829e-04 |
-0.030499587 |
significant |
| D0606 |
0.003034342 |
0.043637689 |
5.152105e-05 |
3.161839e-04 |
-0.040603346 |
significant |
| D0607 |
0.008552574 |
0.041697052 |
2.096373e-03 |
4.840715e-03 |
-0.033144478 |
significant |
| D0608 |
0.000000000 |
0.002444638 |
3.625355e-03 |
7.547870e-03 |
-0.002444638 |
significant |
| D0609 |
0.158687978 |
0.084565489 |
1.403581e-04 |
5.401660e-04 |
0.074122489 |
significant |
| D0610 |
0.000000000 |
0.007160308 |
1.522953e-03 |
3.916339e-03 |
-0.007160308 |
significant |
| D0702 |
0.455688142 |
0.326464654 |
2.635404e-02 |
4.236663e-02 |
0.129223488 |
significant |
| D0705 |
0.323241782 |
0.258159545 |
2.306458e-03 |
5.050347e-03 |
0.065082237 |
significant |
| D0706 |
0.059976159 |
0.148351231 |
1.665215e-03 |
3.916339e-03 |
-0.088375073 |
significant |
| D0807 |
0.015074471 |
0.041747020 |
8.978999e-05 |
4.072617e-04 |
-0.026672549 |
significant |
| D0816 |
0.043641253 |
0.091390659 |
8.340902e-04 |
2.407488e-03 |
-0.047749406 |
significant |
| D0817 |
0.005827776 |
0.019925548 |
1.445050e-03 |
3.823361e-03 |
-0.014097772 |
significant |
| D0907 |
0.162914556 |
0.328681837 |
9.967024e-04 |
2.751765e-03 |
-0.165767281 |
significant |
| D0908 |
0.305403136 |
0.623522292 |
3.432236e-05 |
2.564082e-04 |
-0.318119156 |
significant |
| D0910 |
0.124407773 |
0.396891803 |
1.125340e-04 |
4.610265e-04 |
-0.272484030 |
significant |

| term |
df |
SumOfSqs |
R2 |
statistic |
p.value |
| environment |
1 |
18.732917 |
0.27521376 |
11.099093 |
0.001 |
| river |
2 |
5.451379 |
0.08008868 |
1.614948 |
0.148 |
| Residual |
26 |
43.882492 |
0.64469756 |
NA |
NA |
| Total |
29 |
68.066788 |
1.00000000 |
NA |
NA |


Marginal


Wilcoxon rank sum exact test
data: mci by environment
W = 56, p-value = 0.03277
alternative hypothesis: true location shift is not equal to 0
# A tibble: 33 × 5
gift high low p_value p_adjust
<chr> <dbl> <dbl> <dbl> <dbl>
1 B0221 0.177 0.527 0.00268 0.0297
2 B0605 0.0886 0.396 0.00663 0.0371
3 B0709 0 0.0329 0.000980 0.0196
4 B1028 0.00776 0.0891 0.00224 0.0297
5 D0101 0.00637 0.0634 0.00261 0.0297
6 D0103 0.0689 0.216 0.00839 0.0435
7 D0201 0.0589 0.252 0.00276 0.0297
8 D0202 0.110 0.323 0.00820 0.0435
9 D0203 0.214 0.424 0.00932 0.0450
10 D0204 0.225 0.483 0.00631 0.0368
# ℹ 23 more rows
| gift |
high |
low |
p_value |
p_adjust |
difference |
significance |
| B0221 |
0.177144655 |
0.52701883 |
2.677235e-03 |
0.029723307 |
-0.34987417 |
significant |
| B0605 |
0.088604745 |
0.39623709 |
6.627165e-03 |
0.037112124 |
-0.30763234 |
significant |
| B0709 |
0.000000000 |
0.03290746 |
9.803796e-04 |
0.019607591 |
-0.03290746 |
significant |
| B1028 |
0.007758061 |
0.08906279 |
2.236734e-03 |
0.029723307 |
-0.08130473 |
significant |
| D0101 |
0.006371054 |
0.06336166 |
2.607633e-03 |
0.029723307 |
-0.05699060 |
significant |
| D0103 |
0.068861705 |
0.21552103 |
8.387951e-03 |
0.043493078 |
-0.14665933 |
significant |
| D0201 |
0.058876923 |
0.25212032 |
2.760021e-03 |
0.029723307 |
-0.19324339 |
significant |
| D0202 |
0.110126355 |
0.32295102 |
8.200669e-03 |
0.043493078 |
-0.21282467 |
significant |
| D0203 |
0.213841081 |
0.42380897 |
9.322462e-03 |
0.045004989 |
-0.20996789 |
significant |
| D0204 |
0.225490303 |
0.48319440 |
6.306083e-03 |
0.036785487 |
-0.25770410 |
significant |
| D0205 |
0.064765347 |
0.17098720 |
9.226787e-03 |
0.045004989 |
-0.10622185 |
significant |
| D0208 |
0.113400443 |
0.25966177 |
4.235560e-03 |
0.032943248 |
-0.14626132 |
significant |
| D0209 |
0.101669651 |
0.28432354 |
1.758623e-03 |
0.027356358 |
-0.18265389 |
significant |
| D0212 |
0.172471768 |
0.29920957 |
1.048346e-02 |
0.046042591 |
-0.12673780 |
significant |
| D0213 |
0.094273805 |
0.26659680 |
3.217419e-03 |
0.030029244 |
-0.17232300 |
significant |
| D0301 |
0.000000000 |
0.07340385 |
6.096424e-03 |
0.036785487 |
-0.07340385 |
significant |
| D0302 |
0.004398428 |
0.08700617 |
2.275440e-04 |
0.006371233 |
-0.08260774 |
significant |
| D0304 |
0.009925215 |
0.22320473 |
5.606955e-05 |
0.002616579 |
-0.21327951 |
significant |
| D0307 |
0.015936692 |
0.24428734 |
1.718478e-04 |
0.006014672 |
-0.22835064 |
significant |
| D0308 |
0.119232778 |
0.32375506 |
5.354418e-03 |
0.036785487 |
-0.20452228 |
significant |
| D0502 |
0.059489928 |
0.55204220 |
1.708261e-05 |
0.001195782 |
-0.49255228 |
significant |
| D0503 |
0.018551988 |
0.14195916 |
1.085290e-02 |
0.046042591 |
-0.12340717 |
significant |
| D0509 |
0.273367616 |
0.53084131 |
1.054085e-02 |
0.046042591 |
-0.25747370 |
significant |
| D0518 |
0.092542585 |
0.27773775 |
4.091025e-03 |
0.032943248 |
-0.18519517 |
significant |
| D0603 |
0.000000000 |
0.10245182 |
6.096424e-03 |
0.036785487 |
-0.10245182 |
significant |
| D0607 |
0.009275994 |
0.10179302 |
6.834773e-04 |
0.015947804 |
-0.09251702 |
significant |
| D0611 |
0.000000000 |
0.14970317 |
6.096424e-03 |
0.036785487 |
-0.14970317 |
significant |
| D0704 |
0.163489399 |
0.43901684 |
3.994756e-03 |
0.032943248 |
-0.27552744 |
significant |
| D0706 |
0.003315462 |
0.10115430 |
5.202430e-03 |
0.036785487 |
-0.09783884 |
significant |
| D0807 |
0.006303428 |
0.21451084 |
1.251696e-05 |
0.001195782 |
-0.20820742 |
significant |
| D0816 |
0.035535517 |
0.22732398 |
3.217419e-03 |
0.030029244 |
-0.19178846 |
significant |
| D0817 |
0.002166158 |
0.01700964 |
1.492190e-03 |
0.026113324 |
-0.01484348 |
significant |
| D0904 |
0.095233051 |
0.00000000 |
1.053795e-02 |
0.046042591 |
0.09523305 |
significant |

| term |
df |
SumOfSqs |
R2 |
statistic |
p.value |
| environment |
1 |
22.66237 |
0.11067970 |
3.415318 |
0.021 |
| river |
2 |
16.20626 |
0.07914899 |
1.221177 |
0.235 |
| Residual |
25 |
165.88774 |
0.81017131 |
NA |
NA |
| Total |
28 |
204.75637 |
1.00000000 |
NA |
NA |

